Pub Date : 2022-12-16DOI: 10.25073/2588-1086/vnucsce.298
Loc Xuan Pham, T. Bui, T. D. Chu
The demand for a compact, easy-to-use and precise thermal cycler is always extremely high in biomedical field due to the decisive role of temperature in determining the accuracy of many biomedical applications. In this study, a new design of thermal cycler is proposed to improve the ease of manipulation as well as production process while maintaining the required accuracy of temperature handling. Specifically, a semiconductor component called Peltier is utilized as the main heat generation source in this work, which offers an operation range of 15-80°C. As Peltier has already been mass produced in the market and gained its popularity by appearing in many home appliances, the production cost and time could be minimized. Additionally, by applying the PID control algorithm, the accuracy of the proposed system could be maintained (maximum variation within 1°C in case of Isothermal Amplification and 2°C in case of Temperature Cycling Amplification) as compared with other thermal cyclers with sophisticated heating technology. The thermal cycler proposed in this work is expected to be further developed to be integrated into the microfluidic chip for rapid virus detection applications.
{"title":"A Thermal Cycler based on Solid-state Active Heat Pump and PID Control Algorithm toward Biomedical Applications","authors":"Loc Xuan Pham, T. Bui, T. D. Chu","doi":"10.25073/2588-1086/vnucsce.298","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.298","url":null,"abstract":"The demand for a compact, easy-to-use and precise thermal cycler is always extremely high in biomedical field due to the decisive role of temperature in determining the accuracy of many biomedical applications. In this study, a new design of thermal cycler is proposed to improve the ease of manipulation as well as production process while maintaining the required accuracy of temperature handling. Specifically, a semiconductor component called Peltier is utilized as the main heat generation source in this work, which offers an operation range of 15-80°C. As Peltier has already been mass produced in the market and gained its popularity by appearing in many home appliances, the production cost and time could be minimized. Additionally, by applying the PID control algorithm, the accuracy of the proposed system could be maintained (maximum variation within 1°C in case of Isothermal Amplification and 2°C in case of Temperature Cycling Amplification) as compared with other thermal cyclers with sophisticated heating technology. The thermal cycler proposed in this work is expected to be further developed to be integrated into the microfluidic chip for rapid virus detection applications.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121651090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.25073/2588-1086/vnucsce.317
Quoc-Loc Duong
Natural Language Inference (NLI) is one of the critical tasks in natural language understanding which we take through the VLSP2021-NLI Shared Task competition. VLSP2021-NLI Shared Task is a competition to improve existing methods for NLI tasks, thereby enhancing the efficiency of applications. One of the challenges of the competition is the dataset in both Vietnamese and English. In this article, we report on evaluating the NLI task of the competition. We first implement the 5-fold cross-validation evaluation method. We following leverage model architectures pre-trained on cross-lingual language datasets such as XLM-RoBERTa and RemBERT to create contextual word embeddings for classification. Our final result reaches 90.00% on the test dataset of the organizers.
{"title":"The VNNLI - VLSP 2021: Leveraging Contextual Word Embedding for NLI Task on Bilingual Dataset","authors":"Quoc-Loc Duong","doi":"10.25073/2588-1086/vnucsce.317","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.317","url":null,"abstract":"Natural Language Inference (NLI) is one of the critical tasks in natural language understanding which we take through the VLSP2021-NLI Shared Task competition. VLSP2021-NLI Shared Task is a competition to improve existing methods for NLI tasks, thereby enhancing the efficiency of applications. One of the challenges of the competition is the dataset in both Vietnamese and English. In this article, we report on evaluating the NLI task of the competition. We first implement the 5-fold cross-validation evaluation method. We following leverage model architectures pre-trained on cross-lingual language datasets such as XLM-RoBERTa and RemBERT to create contextual word embeddings for classification. Our final result reaches 90.00% on the test dataset of the organizers.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123116051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.25073/2588-1086/vnucsce.336
Văn Nhân Đặng, Minh Le Nguyen
Machine Reading Comprehension (MRC) has recently made significant progress. This paper is the result of our participation in building an MRC system specifically for Vietnamese on Vietnamese Machine Reading Comprehension at the 8th International Workshop on Vietnamese Language and Speech Processing (VLSP 2021). Based on SQuAD2.0, the organizing committee developed the Vietnamese Question Answering Dataset UIT-ViQuAD2.0, a reading comprehension dataset consisting of questions posed by crowd-workers on a set of Wikipedia Vietnamese articles. The UIT-ViQuAD2.0 dataset evolved from version 1.0 with the difference that version 2.0 contained answerable and unanswerable questions. The challenge of this problem is to distinguish between answerable and unanswerable questions. The answer to every question is a span of text, from the corresponding reading passage, or the question might be unanswerable. Our system employs simple yet highly effective methods. The system uses a pre-trained language model called XLM-RoBERTa (XLM-R), combined with filtering results from multiple output files to produce the final result. We created about 5-7 output files and select the answers with the most repetitions as the final prediction answer. After filtering, our system increased from 75.172% to 76.386% at the F1 measure and achieved 65,329% in the EM measure on the Private Test set.
{"title":"ViMRC - VLSP 2021: Using XLM-RoBERTa and Filter Output for Vietnamese Machine Reading Comprehension","authors":"Văn Nhân Đặng, Minh Le Nguyen","doi":"10.25073/2588-1086/vnucsce.336","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.336","url":null,"abstract":"Machine Reading Comprehension (MRC) has recently made significant progress. This paper is the result of our participation in building an MRC system specifically for Vietnamese on Vietnamese Machine Reading Comprehension at the 8th International Workshop on Vietnamese Language and Speech Processing (VLSP 2021). Based on SQuAD2.0, the organizing committee developed the Vietnamese Question Answering Dataset UIT-ViQuAD2.0, a reading comprehension dataset consisting of questions posed by crowd-workers on a set of Wikipedia Vietnamese articles. The UIT-ViQuAD2.0 dataset evolved from version 1.0 with the difference that version 2.0 contained answerable and unanswerable questions. The challenge of this problem is to distinguish between answerable and unanswerable questions. The answer to every question is a span of text, from the corresponding reading passage, or the question might be unanswerable. Our system employs simple yet highly effective methods. The system uses a pre-trained language model called XLM-RoBERTa (XLM-R), combined with filtering results from multiple output files to produce the final result. We created about 5-7 output files and select the answers with the most repetitions as the final prediction answer. After filtering, our system increased from 75.172% to 76.386% at the F1 measure and achieved 65,329% in the EM measure on the Private Test set.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"18 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120921725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.25073/2588-1086/vnucsce.334
Nhat Nguyen Duy, Phong Nguyen-Thuan Do
The development of industry 4.0 in the world is creating challenges in Artificial Intelligence (AI) in general and Natural Language Processing (NLP) in particular. Machine Reading Comprehension (MRC) is an NLP task with real-world applications that require machines to determine the correct answers to questions based on a given document. MRC systems must not only answer questions when possible but also determine when no answer is supported by the document and abstain from answering. In this paper, we present the description of our system to solve this task at the VLSP shared task 2021: Vietnamese Machine Reading Comprehension with UIT-ViQuAD 2.0. We propose a model to solve that task, called MRC4MRC. The model is a combination of two MRC components. Our MRC4MRC based on the XLM-RoBERTa pre-trained language model is 79.13% of F1-score (F1) and 69.72% of EM (Exact Match) on the public-test set. Our experiments also show that the XLM-R language model is better than the powerful PhoBERT language model on UIT-ViQuAD 2.0.
{"title":"ViMRC VLSP 2021: XLM-R versus PhoBERT on Vietnamese Machine Reading Comprehension","authors":"Nhat Nguyen Duy, Phong Nguyen-Thuan Do","doi":"10.25073/2588-1086/vnucsce.334","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.334","url":null,"abstract":"The development of industry 4.0 in the world is creating challenges in Artificial Intelligence (AI) in general and Natural Language Processing (NLP) in particular. Machine Reading Comprehension (MRC) is an NLP task with real-world applications that require machines to determine the correct answers to questions based on a given document. MRC systems must not only answer questions when possible but also determine when no answer is supported by the document and abstain from answering. In this paper, we present the description of our system to solve this task at the VLSP shared task 2021: Vietnamese Machine Reading Comprehension with UIT-ViQuAD 2.0. We propose a model to solve that task, called MRC4MRC. The model is a combination of two MRC components. Our MRC4MRC based on the XLM-RoBERTa pre-trained language model is 79.13% of F1-score (F1) and 69.72% of EM (Exact Match) on the public-test set. Our experiments also show that the XLM-R language model is better than the powerful PhoBERT language model on UIT-ViQuAD 2.0.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121506340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.25073/2588-1086/vnucsce.346
Quan Quoc Chu, Vi Van Ngo, N. H. Le, Duc Sy Nguyen
In recent years, there are multiple systems (eg. search engines and dialogue systems) that require machines to be able to read and understand human text to serve several tasks in application. Machine Reading Comprehension (MRC) has posed a challenge to the Natural Language Processing (NLP) community in teaching machines to understand the meaning of human text in order to answer questions provided. Specifically in this challenge, the dataset contains questions that can be unanswerable, otherwise the answers can be extracted from the given passages. To deal with this challenge, our works mainly based on a recent approach, known as Retrospective Reader, to confronting unanswerable questions. Additionally, we focuses on enhancing the ability of answer extraction by applying properly attention mechanism and improving the representation ability through semantic information. Besides, we also present an ensemble way to acquire significant improvement in results provided by single models. Our method achieves 1$^{st}$ place on Vietnamese MRC shared task at the $8^{th}$ International Workshop on Vietnamese Language and Speech Processing (VLSP) with F1-score of textbf{0.77241} and exact match (EM) of textbf{0.66137} on the private test phase. For research purpose, our source code is available at url{https://github.com/NamCyan/MRC_VLSP2021}
{"title":"ViMRC - VLSP 2021: Improving Retrospective Reader for Vietnamese Machine Reading Comprehension","authors":"Quan Quoc Chu, Vi Van Ngo, N. H. Le, Duc Sy Nguyen","doi":"10.25073/2588-1086/vnucsce.346","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.346","url":null,"abstract":"In recent years, there are multiple systems (eg. search engines and dialogue systems) that require machines to be able to read and understand human text to serve several tasks in application. Machine Reading Comprehension (MRC) has posed a challenge to the Natural Language Processing (NLP) community in teaching machines to understand the meaning of human text in order to answer questions provided. Specifically in this challenge, the dataset contains questions that can be unanswerable, otherwise the answers can be extracted from the given passages. To deal with this challenge, our works mainly based on a recent approach, known as Retrospective Reader, to confronting unanswerable questions. Additionally, we focuses on enhancing the ability of answer extraction by applying properly attention mechanism and improving the representation ability through semantic information. Besides, we also present an ensemble way to acquire significant improvement in results provided by single models. Our method achieves 1$^{st}$ place on Vietnamese MRC shared task at the $8^{th}$ International Workshop on Vietnamese Language and Speech Processing (VLSP) with F1-score of textbf{0.77241} and exact match (EM) of textbf{0.66137} on the private test phase. For research purpose, our source code is available at url{https://github.com/NamCyan/MRC_VLSP2021}","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132431092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.25073/2588-1086/vnucsce.316
Thi-Thu-Hong Le
MRC is challenging the natural language processing fields; machines automatically have to answer questions based on specific passages for this task. In recent years, machine reading comprehension (MRC) has received much attention; many articles have been written about this task. However, most of those articles only develop models in two main languages, English and Chinese. In this article, we propose to apply a new model to the task of reading comprehension in Vietnamese. Specifically, we use BLANC (BLock AttentioN for Context prediction) on pre-trained baseline models to solve the Machine reading comprehension (MRC) task on Vietnamese. We have achieved good results when using BLANC on the baseline model. Specifically, with the MRC task at the VLSP share-task 2021, we scored 76.877% of F1-score on the private test and ranked 2nd in the total. This shows that BLANC works very well in MRC tasks and further enhances the Vietnamese MRC development.
MRC正在挑战自然语言处理领域;为了完成这项任务，机器必须根据特定的段落自动回答问题。近年来，机器阅读理解(MRC)备受关注;关于这个任务已经写了很多文章。然而，这些文章大多只开发了两种主要语言的模型，英语和中文。在本文中，我们提出了一种新的模式应用于越南语阅读理解任务。具体来说，我们在预训练的基线模型上使用BLANC (BLock AttentioN for Context prediction)来解决越南语的机器阅读理解(MRC)任务。我们在基线模型上使用BLANC取得了很好的效果。具体来说，在VLSP共享任务2021的MRC任务中，我们在私测中得分为f1的76.877%，排名第二。这表明BLANC在MRC任务中工作得很好，并进一步促进了越南MRC的发展。
{"title":"ViMRC - VLSP 2021: Context-Aware Answer Extraction in Vietnamese Question Answering","authors":"Thi-Thu-Hong Le","doi":"10.25073/2588-1086/vnucsce.316","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.316","url":null,"abstract":"MRC is challenging the natural language processing fields; machines automatically have to answer questions based on specific passages for this task. In recent years, machine reading comprehension (MRC) has received much attention; many articles have been written about this task. However, most of those articles only develop models in two main languages, English and Chinese. In this article, we propose to apply a new model to the task of reading comprehension in Vietnamese. Specifically, we use BLANC (BLock AttentioN for Context prediction) on pre-trained baseline models to solve the Machine reading comprehension (MRC) task on Vietnamese. We have achieved good results when using BLANC on the baseline model. Specifically, with the MRC task at the VLSP share-task 2021, we scored 76.877% of F1-score on the private test and ranked 2nd in the total. This shows that BLANC works very well in MRC tasks and further enhances the Vietnamese MRC development.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123726115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.25073/2588-1086/vnucsce.446
T. Le, T. Bui, The Ngoc Dang
We present a new method for image compression in all-optical domain. The new hardware architecture is suitable for directly integrating with digital cameras for image processing. The proposed architecture is based on the optical Haar wavelet transform (HWT) using only one 4x4 multimode interference (MMI) coupler cascaded with a 2x2 MMI coupler. The processing of images therefore is at very high speed.
{"title":"On-chip All-optical Haar Transform based on a 4x4 MMI coupler cascaded with a 2x2 MMI coupler for Image Compression","authors":"T. Le, T. Bui, The Ngoc Dang","doi":"10.25073/2588-1086/vnucsce.446","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.446","url":null,"abstract":"We present a new method for image compression in all-optical domain. The new hardware architecture is suitable for directly integrating with digital cameras for image processing. The proposed architecture is based on the optical Haar wavelet transform (HWT) using only one 4x4 multimode interference (MMI) coupler cascaded with a 2x2 MMI coupler. The processing of images therefore is at very high speed.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128465786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.25073/2588-1086/vnucsce.363
Q. T. Ngo, Anh Tuan Hoang, Huyen Nguyen, Lien Nguyen
This paper presents the first challenge on recognizing textual entailment (RTE), also known as natural language inference (NLI), held in a Vietnamese Language and Speech Processing workshop (VLSP 2021).The challenge aims to determine, for a given pair of sentences, whether the two sentences semantically agree, disagree, or are neutral/irrelevant to each other. The input sentences are in English or Vietnamese and may not be in the same language. This task is important in identifying, from different information sources, the evidence that supports or refutes a statement. The identification of such evidence is subsequently useful for many information tracking applications, such as opinion mining, brand and reputation management, and particularly fighting against fake news.Through this challenge, we would like to provide an opportunity for participants who are interested in the problem, to contribute their knowledge to improve the existing techniques and methods for the task, so as to enhance the effectiveness of those applications.In the paper, we introduce a collection of Vietnamese and English sentences in the domain of health that we built to serve as a benchmarking dataset for the task. We also describe the evaluation results of systems participating in the challenge.
{"title":"VLSP 2021 - vnNLI Challenge: Vietnamese and English-Vietnamese Textual Entailment","authors":"Q. T. Ngo, Anh Tuan Hoang, Huyen Nguyen, Lien Nguyen","doi":"10.25073/2588-1086/vnucsce.363","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.363","url":null,"abstract":"This paper presents the first challenge on recognizing textual entailment (RTE), also known as natural language inference (NLI), held in a Vietnamese Language and Speech Processing workshop (VLSP 2021).The challenge aims to determine, for a given pair of sentences, whether the two sentences semantically agree, disagree, or are neutral/irrelevant to each other. The input sentences are in English or Vietnamese and may not be in the same language. This task is important in identifying, from different information sources, the evidence that supports or refutes a statement. The identification of such evidence is subsequently useful for many information tracking applications, such as opinion mining, brand and reputation management, and particularly fighting against fake news.Through this challenge, we would like to provide an opportunity for participants who are interested in the problem, to contribute their knowledge to improve the existing techniques and methods for the task, so as to enhance the effectiveness of those applications.In the paper, we introduce a collection of Vietnamese and English sentences in the domain of health that we built to serve as a benchmarking dataset for the task. We also describe the evaluation results of systems participating in the challenge.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121661122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.25073/2588-1086/vnucsce.309
Huy Phi Cong, Xiem Hoang Van, Duong Trieu Dinh
In this paper, we propose a novel multiple description coding (MDC) method, which offers benefits of the new H.265/HEVC video coding standard combined with path diversity systems for robust video transmissions. In the proposed method, two descriptions including odd and even video subsequences are encoded using H.265/HEVC coder and then transmitted over two distinct channels of a path diversity system. At the receiver, the proposed MDC decoder is designed using a novel concept of distributed video coding (DVC) to provide a high image quality for the reconstructed description. Experimental results show that the proposed method can achieve a wide range of tradeoffs between coding efficiency and error resilience, and provide much better H.265/HEVC quality of experiences (QoEs) for users than other conventional MDC methods results
{"title":"HEVC Compatible Multiple Description Coding for Robust Video Transmission over Lossy Networks","authors":"Huy Phi Cong, Xiem Hoang Van, Duong Trieu Dinh","doi":"10.25073/2588-1086/vnucsce.309","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.309","url":null,"abstract":"In this paper, we propose a novel multiple description coding (MDC) method, which offers benefits of the new H.265/HEVC video coding standard combined with path diversity systems for robust video transmissions. In the proposed method, two descriptions including odd and even video subsequences are encoded using H.265/HEVC coder and then transmitted over two distinct channels of a path diversity system. At the receiver, the proposed MDC decoder is designed using a novel concept of distributed video coding (DVC) to provide a high image quality for the reconstructed description. Experimental results show that the proposed method can achieve a wide range of tradeoffs between coding efficiency and error resilience, and provide much better H.265/HEVC quality of experiences (QoEs) for users than other conventional MDC methods results","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133244750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.25073/2588-1086/vnucsce.341
Thao Minh Le, Long Hoang Dang, Thanh-Son Nguyen, Huyen Nguyen, Xuan-Son Vu
This paper presents VieCap4H, a grand data challenge on automatic image caption generation for the healthcare domain in Vietnamese. VieCap4H is held as part of the eighth annual workshop on VietnameseLanguage and Speech Processing (VLSP 2021). The task is considered as an image captioning task. Given a static image, mostly about healthcare-related scenarios, participants are asked to design machine learning methods to generate natural language captions in Vietnamese to describe the visual content of the image. We introduce VieCap4H, a novel human-annotated image captioning dataset in Vietnamese that contains over 10,000 image-caption pairs collected from real-world scenarios in the healthcare domain. All the models proposed by the challenge participants are evaluated using BLEU scores against groundtruths. The challenge was run on AIHUB.VN platform. Within less than two months, the challenge has attracted over 90 individual participants and recorded more than 900 valid submissions.
{"title":"VLSP 2021 - VieCap4H Challenge: Automatic Image Caption Generation for Healthcare Domain in Vietnamese","authors":"Thao Minh Le, Long Hoang Dang, Thanh-Son Nguyen, Huyen Nguyen, Xuan-Son Vu","doi":"10.25073/2588-1086/vnucsce.341","DOIUrl":"https://doi.org/10.25073/2588-1086/vnucsce.341","url":null,"abstract":"This paper presents VieCap4H, a grand data challenge on automatic image caption generation for the healthcare domain in Vietnamese. VieCap4H is held as part of the eighth annual workshop on VietnameseLanguage and Speech Processing (VLSP 2021). The task is considered as an image captioning task. Given a static image, mostly about healthcare-related scenarios, participants are asked to design machine learning methods to generate natural language captions in Vietnamese to describe the visual content of the image. We introduce VieCap4H, a novel human-annotated image captioning dataset in Vietnamese that contains over 10,000 image-caption pairs collected from real-world scenarios in the healthcare domain. All the models proposed by the challenge participants are evaluated using BLEU scores against groundtruths. The challenge was run on AIHUB.VN platform. Within less than two months, the challenge has attracted over 90 individual participants and recorded more than 900 valid submissions. \u0000 ","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122710088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：